Daily CSR

When severe weather disrupts communities, digital connectivity can become an essential lifeline. Communication networks support emergency response, keep critical services operating and help communities remain connected when physical infrastructure is under pressure. Their value can easily go unnoticed during normal conditions, but becomes immediately apparent when connectivity is interrupted.

This year's World Telecommunication and Information Society Day theme, “Digital lifelines – Strengthening resilience in a connected world,” highlights the growing importance of resilient digital infrastructure. Climate change is no longer simply an environmental issue. Increasingly, it represents a direct operational challenge for organizations and societies that depend on interconnected physical and digital systems.

Extreme Weather Is Increasing Pressure on Infrastructure

Climate change is increasingly being experienced through severe storms, flooding, extreme heat and wildfires. These events can place significant and immediate pressure on power systems, communications networks and other essential infrastructure.

Nokia and CGI are conducting a joint assessment examining how extreme weather could affect connectivity infrastructure in the United States and India. The study draws on several sources of information, including World Bank projections, EM-DAT disaster records and primary survey data.

Early findings indicate that extreme weather events are not only becoming more intense but are also occurring in more concentrated and extended patterns. This can create multiple, overlapping pressures on infrastructure rather than producing isolated disruptions.

In the United States, the research identifies an upward trend in severe storm activity. Peak years have recorded as many as 23 major storm events, accompanied by increasingly intense and concentrated rainfall.

In India, the assessment points to growing flood exposure associated with heavier rainfall. The number of days receiving more than 50 mm of rain is projected to increase substantially over the longer term, potentially reaching up to seven times the levels seen in shorter-term baseline periods. At the same time, pre-monsoon temperatures are becoming more persistent and intense.

Higher-emission scenarios amplify these risks in both countries. The findings suggest that extreme weather is increasingly capable of placing sustained, systemic pressure on critical infrastructure rather than causing occasional, isolated interruptions.

Climate adaptation is therefore an ongoing process, while climate resilience represents the desired outcome: infrastructure and services that can anticipate disruption, withstand shocks, recover efficiently and adapt to changing conditions. Technology has an important role in enabling that resilience.

Nokia's Approach to Climate Resilience

Nokia views resilience as the ability of critical systems — including connectivity, energy and public services — to remain operational during periods of severe environmental stress. These systems must also be considered as interconnected networks rather than as individual components operating independently.

The company's approach centers on three areas:

Direct control: Improving the resilience of Nokia's own operations and technologies.
Shared control: Helping customers strengthen the reliability and resilience of their networks and services.
Indirect influence: Working with ecosystem partners to support resilience at the wider community and societal level.

Artificial intelligence is an important enabler across each of these areas. AI can help systems identify potential disruptions, respond dynamically to changing conditions and accelerate recovery. Resilience must also be developed responsibly, with energy efficiency, circularity and careful resource management remaining important considerations so that sustainability and resilience progress together.

Technology for More Resilient Digital Lifelines

The Nokia–CGI assessment identifies several ways extreme weather can affect communications infrastructure.

Higher temperatures and environmental conditions can reduce the performance of communications equipment, while storms and flooding can physically damage network assets. Dependence on electrical grids can also cause a localized infrastructure problem to spread across wider areas. Meanwhile, dangerous conditions and limited physical access can make repairs and recovery more difficult.

Nokia's role includes providing technologies that help customers prepare for, withstand and recover from these disruptions. These capabilities can support business continuity, reduce operational risks and protect long-term economic value. They include:

Next-generation mobile networks with satellite-integrated connectivity: Satellite capabilities can supplement terrestrial networks, helping maintain communications during disasters while extending coverage to remote and difficult-to-reach locations.
High-capacity fiber networks: Fiber infrastructure provides stable, low-latency and energy-efficient data connectivity across large geographic areas.
AI-driven network operations and predictive hardware maintenance: These capabilities can identify potential equipment problems and reduce the need for personnel to visit sites during dangerous conditions.
Resilient distributed cloud architectures: Dynamic traffic management and workload distribution across multiple regions can help maintain digital services during disruptions, including incidents affecting data centers.
Automated LTE/5G-connected drone platforms: Drones can provide real-time situational awareness, inspect infrastructure, assess damage and support emergency response for utilities, transportation networks, public safety organizations and industrial operations.
Environmental monitoring through fiber and situational-awareness technologies: Existing fiber infrastructure and sensing technologies can provide information that supports faster decisions and more effective responses during emergencies.
Mission-critical and private wireless networks: These networks provide secure and highly reliable communications for public safety organizations, utilities and other critical industries.

Keeping Communities Connected During Crises

Ultimately, resilience is about more than keeping infrastructure operational. It is about protecting people and helping communities function during periods of uncertainty.

When communications networks remain available, emergency calls can be completed, hospitals can stay connected, families can communicate and authorities can coordinate response and recovery efforts. Reliable connectivity can therefore have effects that extend well beyond the technology itself, supporting social stability when communities face major disruptions.

Building this level of resilience requires cooperation. No single organization can address the challenge alone. Operators, governments, humanitarian organizations and technology partners must work together to develop and deploy solutions that can strengthen connectivity and reach the communities and locations that need them most.

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Telecom providers are pushing for fast 6G standardization and quick adoption across enterprise and consumer markets, with AI playing a central role.

Artificial intelligence (AI) and machine learning (ML) are expected to be foundational elements of the 6G standard, anticipated around 2028–2029.
Engineers working at the intersection of 6G and AI can accelerate time-to-market by understanding how AI/ML can support 6G design and validation.

The transition to 6G marks a major shift — potentially becoming the first generation of wireless networks built to be AI-native. Because AI will deeply influence how 6G operates, engineers face a new challenge: validating systems that are far more adaptive, intelligent, and fast than previous generations.

This overview outlines how AI can support 6G design validation for teams in communication service providers, mobile operators, technology vendors, and device manufacturers. It also explores emerging applications enabled by AI and 6G, the types of AI techniques involved, and how these tools can streamline design and testing workflows.

What new opportunities will 6G and AI unlock?
AI and 6G together are expected to drive major innovations, including real-time digital twins, advanced manufacturing systems, highly autonomous transport, holographic communication, and widespread edge intelligence. These capabilities align with the visions of the ITU and 3GPP for 2030 and beyond.

Real-time digital twins
With widespread coverage, extremely low latency, and high throughput, 6G paired with AI could create high-fidelity, real-time digital counterparts of physical assets and environments. These digital twins would support modeling, control, analysis, and simulation with unprecedented accuracy. Digital twin networks could mirror actual network conditions to enable continuous optimization, especially when combined with integrated sensing and communication (ISAC).

Smart factories
AI-enhanced 6G connectivity could enable industrial automation at scale through reliable, ultra-responsive data exchange across robotics, industrial IoT, and intelligent devices. “Industrial 6G” may enable fully automated operations in environments such as factories, ports, and airports, supported by private 6G deployments.

Autonomous mobility
Next-generation mobility systems — from autonomous vehicles to intelligent transportation — will rely on AI-powered 6G capabilities. This includes AI-assisted driving, real-time mapping, and precise positioning for cellular vehicle-to-everything (C-V2X) interactions.

Holographic communication
Future 6G and AI infrastructure may support immersive communications such as holographic telepresence and multi-sensory remote interaction. AI-driven semantic communication could reduce bandwidth demands by transmitting only the essential meaning behind data-heavy content.

Distributed edge intelligence
6G is expected to blur the line between communication and computing by pushing AI models to the network edge. This could enable coordinated inference, collaborative robotics, and pervasive, real-time intelligence across devices.

How will AI improve 6G network design and operations?
6G will involve both physical elements (e.g., radios, base stations, user devices) and logical components (e.g., RAN, core network functions, protocol stacks). Many of these will be optimized using AI during design, validation, and even runtime.

AI-native air interface
AI could enhance key radio functions such as channel estimation, symbol detection, beam selection, modulation, and antenna configuration. These models may operate on devices, at the base station, or jointly across both.

AI-assisted beamforming
AI methods may support:

improved channel state information for UM-MIMO
more accurate beam prediction
reduced complexity in beam pairing
optimization of the environment using reconfigurable intelligent surfaces (RIS)

AI-optimized RAN
AI could enable a self-organizing RAN capable of real-time adaptation, end-to-end optimization, and autonomous performance tuning.

Automated network management
AI-driven operations may include predictive maintenance, traffic forecasting, energy optimization, and intelligent resource allocation. Real-time threat detection and mitigation could also be enhanced through AI analytics.

Which AI techniques are most useful for validating 6G performance?
A range of AI methods — deep learning, reinforcement learning, generative models, and more — will support system-level design and testing.

Reinforcement learning (RL)
RL is well-suited for automating decision-making in unpredictable environments and may be applied to:

RAN optimization and mobility management
beamforming prediction
automated functional testing using RL-trained agents
detecting performance bottlenecks through large-scale exploration

Deep neural networks (DNNs)
DNNs may support tasks such as:

advanced channel estimation in challenging environments
channel state information (CSI) compression via CNN-based autoencoders

Transformer models
Transformer autoencoders may enhance CSI compression and feedback efficiency.

Graph neural networks (GNNs)
GNNs can model network topology and spatial relationships for interference control, mobility forecasting, and resource allocation.

Generative adversarial networks (GANs)
GANs can generate realistic channel data, support denoising, and detect anomalies.

Large reasoning/action models
These emerging agentic models may coordinate complex workflows and help test sophisticated, multi-component 6G systems.

How will synthetic AI data support 6G testing and validation?
AI-generated data will be crucial for exploring the huge range of possible 6G conditions — many of which cannot be physically tested early on.

Key synthetic-data methods include:

Digital twins: full-scale virtual replicas of networks
Generative AI: GAN-based wireless channel synthesis
Specialized testbeds: simulated sub-THz scenarios
Propagation simulators: ray-tracing tools that mimic real-world environments
System-level tools: integrated platforms that combine analytics, noise, and channel models to produce training datasets

Can AI help validate 6G hardware and chip designs?
Yes. AI-powered anomaly detection, automation, and data-driven modeling could support the design of components for sub-THz frequencies, UM-MIMO, and other 6G features.
Key methods include:

AI-based nonlinear models for complex behaviors
integration of AI into EDA tools for RFIC design
testing and evaluating AI-enabled physical-layer blocks
AI-enhanced beamforming and CSI compression
hardware-in-the-loop testing with channel emulation
anomaly detection during simulation and validation

What challenges come with using AI for 6G validation?
AI’s reliability isn’t guaranteed. Issues include out-of-distribution errors, limited data, low interpretability, overfitting, and hallucinations. To improve trustworthiness:

Ensure AI aligns with established wireless engineering principles
Plan for limited real-world data by augmenting with analytical models
Use interpretable AI methods alongside black-box models
Apply physics-informed constraints to maintain realism
Prevent overfitting through proper data diversification
Use hardware-in-the-loop testing to close the gap between simulation and reality
Mitigate energy, security, and operational risks introduced by AI integration

Keysight’s role
This summary illustrates how AI can support 6G design and testing. Keysight provides tools, research expertise, and 6G-ready test solutions to help engineering teams innovate with confidence throughout development.

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